from __future__ import absolute_import
from __future__ import division

import torch
from torch import nn
from torch.nn import functional as F
import torchvision
import torch.utils.model_zoo as model_zoo


__all__ = ['mlfn']


model_urls = {
    # training epoch = 5, top1 = 51.6
    'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mlfn-9cb5a267.pth.tar',
}


class MLFNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride, fsm_channels, groups=32):
        super(MLFNBlock, self).__init__()
        self.groups = groups
        mid_channels = out_channels // 2
        
        # Factor Modules
        self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
        self.fm_bn1 = nn.BatchNorm2d(mid_channels)
        self.fm_conv2 = nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, bias=False, groups=self.groups)
        self.fm_bn2 = nn.BatchNorm2d(mid_channels)
        self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
        self.fm_bn3 = nn.BatchNorm2d(out_channels)
        
        # Factor Selection Module
        self.fsm = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_channels, fsm_channels[0], 1),
            nn.BatchNorm2d(fsm_channels[0]),
            nn.ReLU(inplace=True),
            nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
            nn.BatchNorm2d(fsm_channels[1]),
            nn.ReLU(inplace=True),
            nn.Conv2d(fsm_channels[1], self.groups, 1),
            nn.BatchNorm2d(self.groups),
            nn.Sigmoid(),
        )

        self.downsample = None
        if in_channels != out_channels or stride > 1:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels),
            )

    def forward(self, x):
        residual = x
        s = self.fsm(x)
        
        # reduce dimension
        x = self.fm_conv1(x)
        x = self.fm_bn1(x)
        x = F.relu(x, inplace=True)

        # group convolution
        x = self.fm_conv2(x)
        x = self.fm_bn2(x)
        x = F.relu(x, inplace=True)

        # factor selection
        b, c = x.size(0), x.size(1)
        n = c // self.groups
        ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
        ss = ss.view(b, n, self.groups, 1, 1)
        ss = ss.permute(0, 2, 1, 3, 4).contiguous()
        ss = ss.view(b, c, 1, 1)
        x = ss * x

        # recover dimension
        x = self.fm_conv3(x)
        x = self.fm_bn3(x)
        x = F.relu(x, inplace=True)

        if self.downsample is not None:
            residual = self.downsample(residual)

        return F.relu(residual + x, inplace=True), s


class MLFN(nn.Module):
    """
    Multi-Level Factorisation Net

    Reference:
    Chang et al. Multi-Level Factorisation Net for Person Re-Identification. CVPR 2018.
    """
    def __init__(self, num_classes, loss={'xent'}, groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs):
        super(MLFN, self).__init__()
        self.loss = loss
        self.groups = groups

        # first convolutional layer
        self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(channels[0])
        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)

        # main body
        self.feature = nn.ModuleList([
            # layer 1-3
            MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
            MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
            MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
            # layer 4-7
            MLFNBlock(channels[1], channels[2], 2, [256, 128], self.groups),
            MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
            MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
            MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
            # layer 8-13
            MLFNBlock(channels[2], channels[3], 2, [512, 128], self.groups),
            MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
            MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
            MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
            MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
            MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
            # layer 14-16
            MLFNBlock(channels[3], channels[4], 2, [512, 128], self.groups),
            MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
            MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
        ])
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)

        # projection functions
        self.fc_x = nn.Sequential(
            nn.Conv2d(channels[4], embed_dim, 1, bias=False),
            nn.BatchNorm2d(embed_dim),
            nn.ReLU(inplace=True),
        )
        self.fc_s = nn.Sequential(
            nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
            nn.BatchNorm2d(embed_dim),
            nn.ReLU(inplace=True),
        )

        self.classifier = nn.Linear(embed_dim, num_classes)
        
        self.init_params()

    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x, inplace=True)
        x = self.maxpool(x)

        s_hat = []
        for block in self.feature:
            x, s = block(x)
            s_hat.append(s)
        s_hat = torch.cat(s_hat, 1)

        x = self.global_avgpool(x)
        x = self.fc_x(x)
        s_hat = self.fc_s(s_hat)

        v = (x + s_hat) * 0.5
        v = v.view(v.size(0), -1)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == {'xent'}:
            return y
        elif self.loss == {'xent', 'htri'}:
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, model_url):
    """
    Initialize model with pretrained weights.
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)
    print("Initialized model with pretrained weights from {}".format(model_url))


def mlfn(num_classes, loss, pretrained='imagenet', **kwargs):
    model = MLFN(num_classes, loss, **kwargs)
    if pretrained == 'imagenet':
        init_pretrained_weights(model, model_urls['imagenet'])
    return model